# Import useful modules
import numpy as np
import pandas as pd
import scanpy as sc
import os
#import igraph
import matplotlib.pyplot as plt
import seaborn
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_versions()
sc.settings.set_figure_params(dpi=130)
adata_ann = sc.read_h5ad('/Data/Annotated_dataset_v1.h5ad')
metadata = pd.read_csv('/Data/Annotated_dataset_metadata.tsv', sep = '\t')
adata_ann.obs['CellType.Corrected'] = metadata['CellType.Corrected']
adata_ann.obs['CellType.Corrected_rare'] = metadata['CellType.Corrected_rare']
cellsid = pd.read_csv('/Data/PAGA_subset_Bronchial.txt', sep = '\t')
adata = adata_ann[cellsid['x'], ]
adata.shape
sc.pp.filter_genes(adata, min_cells=10)
adata.X.shape
sc.tl.pca(adata, svd_solver='arpack')
sc.pl.pca_loadings(adata, components=list(range(0,16)))
sc.pl.pca_variance_ratio(adata, log = False)
sc.pp.neighbors(adata, n_neighbors=10, n_pcs=6)
sc.tl.umap(adata)
sc.pl.umap(adata, color=['CellType.Corrected'], edges = False)
sc.tl.paga(adata, groups='CellType.Corrected_rare')
sc.pl.paga(adata)
sc.tl.draw_graph(adata, init_pos='paga')
sc.tl.draw_graph(adata)
sc.pl.draw_graph(adata, color='CellType.Corrected_rare', edges = True, save = 'paga_bronchial.pdf')
sc.pl.draw_graph(adata, color='CellType.Corrected', edges = True, save = 'paga_bronchial_double.pdf')
sc.pl.paga_compare(
adata, threshold=0.03, title='', right_margin=0.2, size=10, edge_width_scale=0.5,
legend_fontsize=0, fontsize=0, frameon=False, edges=True, save='paga_bronchial_mix.pdf')
adata.write('/Data/anndata_V6_Paga_bronchial_subset.h5ad')
adata.uns['CellType.Corrected_colors'] = [
'#A31E22', #, Basal ,
'#F3766E',
'#2da9d2', # Deuterosomal
'#ff00ff', # Mucous Multiciliated cells
'#466cb9', # Multiciliated '#90a7d5',
'#53c653', # Secretory , '#a9c653'
'#FCCC0A'] # Suprabasal, '#e0c96c']
adata.uns['CellType.Corrected_rare_colors'] = [
'#A31E22', #, Basal ,
'#F3766E',
'#2da9d2', # Deuterosomal
'#466cb9', # Multiciliated '#90a7d5',
'#53c653', # Secretory , '#a9c653'
'#FCCC0A'] # Suprabasal, '#e0c96c']
cellsid = pd.read_csv('/Data/PAGA_subset_Nasal.txt', sep = '\t')
adata = adata_ann[cellsid['x'], ]
adata.shape
sc.pp.filter_genes(adata, min_cells=10)
adata.X.shape
sc.tl.pca(adata, svd_solver='arpack')
sc.pl.pca_loadings(adata, components=list(range(0,16)))
sc.pl.pca_variance_ratio(adata, log = False)
sc.pp.neighbors(adata, n_neighbors=10, n_pcs=6)
sc.tl.umap(adata)
sc.pl.umap(adata, color=['CellType.Corrected'], edges = False)
sc.tl.paga(adata, groups='CellType.Corrected_rare')
sc.pl.paga(adata)
sc.tl.draw_graph(adata, init_pos='paga')
sc.tl.draw_graph(adata)
sc.pl.draw_graph(adata, color='CellType.Corrected', edges = True, save = 'paga_nasal_double.pdf')
sc.pl.draw_graph(adata, color='CellType.Corrected_rare', edges = True, save = 'paga_nasal.pdf')
sc.pl.paga_compare(
adata, threshold=0.03, title='', right_margin=0.2, size=10, edge_width_scale=0.5,
legend_fontsize=0, fontsize=0, frameon=False, edges=True, save = 'paga_nasal_mix.pdf')
adata.write('/Data/anndata_V6_Paga_nasal_subset.h5ad')